Manual testicular measurement is a standard part of scrotal ultrasound for infertility assessment, but variations in image plane selection, caliper placement, operator experience, and image quality can make the process time-consuming and affect measurement consistency. This becomes particularly challenging across bilateral examinations, serial follow-ups, and larger research datasets where reproducible measurements are critical.
This white paper explores a deep learning-based approach to automated 2D testicular quantification that segments the testicular boundary and calculates objective morphometric parameters such as area, major and minor axes, equivalent diameter, perimeter, and shape descriptors. By reducing repeated manual measurement and applying consistent criteria across cases, the approach can accelerate measurement, reduce inter-operator variability, and improve reproducibility for clinical follow-up and research. Importantly, the technology is designed as a decision-support and workflow-assistance tool, with clinicians retaining responsibility for reviewing results and clinical interpretation.